Bootstrap Sampling Tutorial

By Kardi Teknomo, PhD.

Bootstrap is a simple but powerful Monte Carlo method to assess statistical accuracy or to estimate distribution from sample's statistics. This tutorial will give you introduction to some practical idea on what is bootstrap method, how to perform bootstrap step by step by hand and with some simple "programming" in MS Excel. By the end of this tutorial, you will gain much better understanding on how to manage your limited number of observations in your sample by creating your own data. Bootstrap sampling is not magic or miracle or cheating but truly useful scientific method. Keep reading!

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What is bootstrap method?

What is boostsrap sampling?
What are the assumption of Bootstrap?
Example Applications of Bootstrap

Bootstrap numerical example (How to use MS Excel to perform Bootstrap method?)

Key ideas of bootstrap sampling
Why do I need Boostrap Sampling?

Sampling and Bootstrap Sampling

What is sample and population?
What you can get from frequency distribution?
Non parametric Bootstrap

What the Strength and Weaknesses of Bootstrap?

Resources on Bootstrap Method

See Also: Monte Carlo Simulation, Machine Learning algorithm, Statistical Independent, Bootstrap computation using R

Note: To gain more understanding of this tutorial, I hope you have refreshed yourself with the meaning of statistical terminology such as distribution, statistical independent, confidence interval, hypothesis testing, goodness of fit of sample to theoretical distribution and ratio of two random variables.

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Preferable reference for this tutorial is

Teknomo, Kardi. Bootstrap Sampling Tutorial. http://people.revoledu.com/kard/ tutorial/bootstrap/